Maciej Cegłowski (previously) gave this talk, "Superintelligence: The Idea That Eats Smart People," at Web Camp Zagreb last October, spending 45 minutes delving into the origin of the idea that computers are going to become apocalyptic, self-programming, superintelligent basilisks that end all live on Earth (and variations on this theme) and then explaining why this fundamentally evidence-free, fuzzy idea has colonized so many otherwise brilliant people -- including people like Stephen Hawking -- and why it's an irrational and potentially harmful belief system. Read the rest
The Data & Society institute (dedicated to critical, interdisciplinary perspectives on big data) held an online seminar devoted to Cathy O'Neil's groundbreaking book Weapons of Math Destruction, which showed how badly designed algorithmic decision-making systems can create, magnify and entrench the social problems they're supposed to solve, perpetuating inequality, destabilizing the economy, and making a small number of people very, very rich. Read the rest
It's not bad. In fact, this is a triumph: a Christmas song written entirely by an artificial intelligence at the University of Toronto. Yet it has that uncanny neural network je ne sais quoi in spades.
I swear it’s Christmas Eve I hope that’s what you say The best Christmas present in the world is a blessing I’ve always been there for the rest of our lives.Read the rest
In SoundNet: Learning Sound Representations from Unlabeled Video, researchers from MIT's computer science department describe their success in using software image-recognition to automate sound recognition: once software can use video analysis to decide what's going on in a clip, it can then use that understanding to label the sounds in the clip, and thus accumulate a model for understanding sound, without a human having to label videos first for training purposes. Read the rest
Train the Deep Learning Ahem Detector with two sets of audio files, "a negative sample with clean voice/sound" (minimum 3 minutes) and "a positive one with 'ahem' sounds concatenated" (minimum 10s) and it will detect "ahems" in any voice sample thereafter. Read the rest
In Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition, researchers from Carnegie-Mellon and UNC showed how they could fool industrial-strength facial recognition systems (including Alibaba's "smile to pay" transaction system) by printing wide, flat glasses frames with elements of other peoples' faces with "up to 100% success." Read the rest
The Allen Institute for Artificial Intelligence (AI2), funded by billionaire Paul Allen's, is developing projects like an AI-based search engine for scientific papers and a system to extract "visual knowledge" from images and videos. According to Scientific American, another goal of AI2 is "to counter messages perpetuated by Hollywood and even other researchers that AI could menace the human race." SciAm's Larry Greenemeier interviewed AI2 CEO and computer scientist Oren Etzioni:
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Why do so many well-respected scientists and engineers warn that AI is out to get us?
It’s hard for me to speculate about what motivates somebody like Stephen Hawking or Elon Musk to talk so extensively about AI. I’d have to guess that talking about black holes gets boring after awhile—it’s a slowly developing topic. The one thing that I would say is that when they and Bill Gates—someone I respect enormously—talk about AI turning evil or potential cataclysmic consequences, they always insert a qualifier that says “eventually” or this “could” happen. And I agree with that. If we talk about a thousand-year horizon or the indefinite future, is it possible that AI could spell out doom for the human race? Absolutely it’s possible, but I don’t think this long-term discussion should distract us from the real issues like AI and jobs and AI and weapons systems. And that qualifier about “eventually” or “conceptually” is what gets lost in translation...
How do you ensure that an AI program will behave legally and ethically?
If you’re a bank and you have a software program that’s processing loans, for example, you can’t hide behind it.
The Nightmare Machine is an MIT project to use machine learning image-processing to make imagery for Hallowe'en. Read the rest
Yahoo has released a machine-learning model called open_nsfw that is designed to distinguish not-safe-for-work images from worksafe ones. By tweaking the model and combining it with places-CNN, MIT's scene-recognition model, Gabriel Goh created a bunch of machine-generated scenes that score high for both models -- things that aren't porn, but look porny. Read the rest
The internet's army of enraged anime avatars has a new enemy beyond their comprehension: a Twitter bot created by writer and activist Sarah Nyberg to make fools of them. Some lose themselves to hours of interaction, unaware they are ranting at a computer program. Read the rest